Discrete Manufacturing Processes Research Articles

Digital thread-driven cloud-fog-edge collaborative disturbance mitigation mechanism for adaptive production in digital twin discrete manufacturing workshop

The collaborative capacity among various computing layers within a digital twin workshop directly impacts the handling of disturbances in discrete manufacturing processes. However, the intricate nature of the cross-space (physical and virtual space) and cross-layer (cloud, fog, and edge) interactions within a digital twin system poses challenges to construct a comprehensive data model and interoperable data flow, leading to a fragmented state of computing layers. To tackle these challenges, this study presents a digital thread-driven theory and method for cloud-fog-edge collaboration in digital twin discrete manufacturing workshops. Firstly, the five-dimensions production information that cross spaces and layers is concatenated and integrated by constructing digital thread models. Subsequently, a disturbance response model is conducted based on these digital thread models to detect, track, and evaluate disturbances. Furthermore, corresponding cloud-fog-edge collaboration strategies are devised for different types and severity of disturbances, facilitating the dynamic production tasks adjustments. Finally, a case study to discuss the performance of the proposed method regarding the maturity of the digital thread model, disturbance response capability, and production self-adaptive adjustment capability. The results demonstrate that the proposed method effectively mitigates makespan and machine idle time under gradual and abrupt disturbances, and saves 46.22% of computing processing time.

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector.

Additive Manufacturing Process Root Selection Using Bayesian Network

Additive manufacturing emerged as a highly promising technology within the realm of manufacturing, offering advantages such as rapid customization, on-demand production, the ability to create intricate shapes, and the absence of economies of scale dependency. Its widespread adoption is evident across various industries, including aviation, oil and gas, and the automotive sector. But finding the most favorable process root to 3D print a part that makes sense both technically and economically is a challenging and tricky task because there are many different methods, each with its own unique constraints. This article focuses on the complex decision-making process of choosing which process root to be selected for 3D printing something, considering various attributes. It introduces a novel framework combining the Delphi methodology, the Neutrosophic Best-Worst Method, and Bayesian Network analysis to discern the most viable additive manufacturing process root for a given part. To validate the efficacy of this framework, a practical case study involving Ferro Oil Tech India Pvt. Ltd. is employed. The study evaluates three discrete additive manufacturing process routes: direct fabrication via direct metal laser sintering, selective laser sintering combined with investment casting, and stereolithography coupled with investment casting. The findings indicate that the direct metal laser sintering process proves to be both economically viable and technically feasible.

QTT-DLSTM: A Cloud-Edge-Aided Distributed LSTM for Cyber-Physical-Social Big Data.

Cyber-physical-social systems (CPSS), an emerging cross-disciplinary research area, combines cyber-physical systems (CPS) with social networking for the purpose of providing personalized services for humans. CPSS big data, recording various aspects of human lives, should be processed to mine valuable information for CPSS services. To efficiently deal with CPSS big data, artificial intelligence (AI), an increasingly important technology, is used for CPSS data processing and analysis. Meanwhile, the rapid development of edge devices with fast processors and large memories allows local edge computing to be a powerful real-time complement to global cloud computing. Therefore, to facilitate the processing and analysis of CPSS big data from the perspective of multi-attributes, a cloud-edge-aided quantized tensor-train distributed long short-term memory (QTT-DLSTM) method is presented in this article. First, a tensor is used to represent the multi-attributes CPSS big data, which will be decomposed into the QTT form to facilitate distributed training and computing. Second, a distributed cloud-edge computing model is used to systematically process the CPSS data, including global large-scale data processing in the cloud, and local small-scale data processed at the edge. Third, a distributed computing strategy is used to improve the efficiency of training via partitioning the weight matrix and large amounts of input data in the QTT form. Finally, the performance of the proposed QTT-DLSTM method is evaluated using experiments on a public discrete manufacturing process dataset, the Li-ion battery dataset, and a public social dataset.

Variation Propagation Analysis of Hull Flat Block Construction Process Based on State Space Model

_ Hull flat block (HFB) construction is a typical discrete manufacturing process, during which block deviation is accumulated. To satisfy the dimensional requirements of shipowners, the variation propagation during HFB construction must be modeled and analyzed. Based on the stream of variation theory, the construction process is analyzed to provide a reference for controlling the deviation. In the hull construction process, the deviation is categorized into part deviation, location deviation, and welding deviation. By analyzing the causes of the different deviations, the different deviations and their accumulations can be calculated. A HFB is used to verify the proposed model, and the results show that the method can be used to calculate the deviation to the HFB. Introduction As a typical large-scale complex equipment, the hull construction process is extremely time-consuming and laborious. The final hull is composed of intermediate products, such as subassemblies, unit assemblies, and grand-assemblies (Cho et al. 1999). Since the group technology was applied in hull construction, a series of production lines has been established based on the process similarity of parts, components, blocks, and grand blocks. The actual dimension of parts would inevitably deviate from the theoretical dimension (Mandal 2017), i.e., dimensional and shape deviations will occur owing to many factors (Okumoto 2002). In the hull construction process, the deviation from the previous process will be propagated to the current process and coupled with the deviation in the current process, thereby affecting the accuracy of the product. Hence, the product must be repaired to satisfy the design requirements (Takechi et al. 1998). This will prolong the production cycle and reduce the production efficiency (Heo et al. 2015). It was discovered that the man-hours of the main operations during hull assembly constituted only one-sixth of the total assembly manhours, whereas the man-hours used for adjustment constituted one-third (Chen et al. 2020). Currently, shipyards need to eliminate the adjustment operation to avoid unnecessary rework, as well as reduce human and material resources in subsequent processes. Tanigawa (2003) estimated that an ideal ship construction accuracy control plan can reduce the production cost by 5%. Therefore, the accuracy of the hull construction process must be controlled and improved in shipyards, and a reasonable accuracy control plan should be devised to improve the product quality.

SSMSPC: self-supervised multivariate statistical in-process control in discrete manufacturing processes

Self-supervised learning has demonstrated state-of-the-art performance on various anomaly detection tasks. Learning effective representations by solving a supervised pretext task with pseudo-labels generated from unlabeled data provides a promising concept for industrial downstream tasks such as process monitoring. In this paper, we present SSMSPC a novel approach for multivariate statistical in-process control (MSPC) based on self-supervised learning. Our motivation for SSMSPC is to leverage the potential of unsupervised representation learning by incorporating self-supervised learning into the general statistical process control (SPC) framework to develop a holistic approach for the detection and localization of anomalous process behavior in discrete manufacturing processes. We propose a pretext task called Location + Transformation prediction, where the objective is to classify both, the type and the location of a randomly applied augmentation on a given time series input. In the downstream task, we follow the one-class classification setting and apply the Hotelling’s T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T^2$$\\end{document} statistic on the learned representations. We further propose an extension to the control chart view that combines metadata with the learned representations to visualize the anomalous time steps in the process data which supports a machine operator in the root cause analysis. We evaluate the effectiveness of SSMSPC with two real-world CNC-milling datasets and show that it outperforms state-of-the-art anomaly detection approaches, achieving 100%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$100\\%$$\\end{document} and 99.6%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$99.6\\%$$\\end{document} AUROC, respectively. Lastly, we deploy SSMSPC at a CNC-milling machine to demonstrate its practical applicability when used as a process monitoring tool in a running process.

Smart factory mapping and design: methodological approaches

Several studies show a demand for further transfer of Industry 4.0 concepts and technologies into practice. This paper describes how companies can be supported methodologically to identify individual opportunities for optimization within their future smart factories. Existing methods are screened, recombined, and enhanced to create a sufficient demand-oriented approach, based on the lean value stream mapping and design. The final approach covers a mapping phase of the current state and a design phase of a future state. It follows the general principle of process optimization prior to digitalization. Being a highly visual method, it can be used to explain the needs and the motivation for the identified improvement initiatives to all involved parties, including non-IT-specialists. This paper also presents an exemplary result and several learnings from tests with three companies with discrete manufacturing processes.

Exploring self-organization and self-adaption for smart manufacturing complex networks

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber—physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.

Digital twin oriented multi-objective flexible job shop scheduling model and its hybrid particle swarm optimization

To solve the problems of low efficiency and insufficient dynamic response of job shop scheduling in the discrete manufacturing process, a multi-objective flexible job shop scheduling model for digital twin and its solution method are proposed. Firstly, a digital twin scheduling model with physical entity, virtual model and production plan is constructed, and four factors are taken as optimization goals. Then, a hybrid particle swarm optimization method is designed to increase the refined optimization ability, and the obtained Pareto optimal solution set is analyzed by grey relational analysis to obtain a satisfactory solution which coincides with the actual production. Finally, a three-dimensional model which is completely mapped with the real job shop scheduling is built by Plant Simulation software. The scheduling process is simulated and optimized by combining with the production data of an enterprise, which verifies the feasibility and applicability of this method, and will effectively guide the production practice.

Reusing and Extending Standards-Based Unit Manufacturing Process Models for Characterizing Sustainability Performance

Abstract Over the past two decades, numerous efforts have characterized manufacturing processes for sustainability performance. These efforts have been pursued primarily by manufacturing researchers in academic and governmental labs, and involve the development of frameworks, methodologies, and standards for characterizing discrete manufacturing processes and their representation as information models. Furthermore, characterization of sustainability performance of manufacturing process flows has been attempted through linking, or composing, these unit manufacturing process (UMP) models. This paper reviews these efforts and identifies existing research gaps that should be addressed by academic, industrial, and governmental researchers. The review includes the relevant sustainable manufacturing standards that have been recently published by ASTM International. A methodology for creating and extending composable models of UMPs that build upon these standards is presented. This research demonstrates how formalization of these prior efforts can address the identified gaps. It is shown that the reuse of UMP models can be enabled by encapsulating specific characteristics of complex processes into information models that can be applied for detailed process analysis and evaluation. This research proposes the concept of a template UMP information model, which can further be abstracted and customized to represent an application-specific, higher-order manufacturing process model. The template model concept is illustrated for manual and computer numerically controlled (CNC) milling processes.

Ball-end milling cutter design method towards the maximum material removal rate under surface roughness constraints

Cutter design and selection, as one of the indispensable tasks in discrete parts' manufacturing process planning, has seldom been investigated in terms of high machining quality and machining efficiency. In order to fill this gap, this paper studies the influences of main geometric parameters of ball-end cutter (helix angle, flute number, cutter radius) on surface roughness and material removal rate (MRR). Firstly, it is revealed that the fundamental reason why the helix angle affects the cutting force coefficients is the oblique angle, not the axial height or axial position angle. And a novel method of cutting force coefficients prediction for newly designed cutter based on local helix angle is proposed. Then, the influences of cutter parameters on surface roughness and MRR are investigated by theoretical and experimental method. Based on this analysis, cutter evaluation models for machining quality and machining efficiency are established, respectively. Particle swarm optimization (PSO) is used as optimization method to search the optimal cutter. Finally, a novel cutter design method combined with cutter evaluation models is proposed to obtain better surface performance and machining efficiency. According to the experimental results, it can be proved that the cutter designed by this method can maximize MRR under the constraint of meeting the parts' surface roughness requirements. This is of great significance for improve parts' performance and reduce machining cost.

Dynamic Monitoring Method of Enterprise Power Consumption Based on Energy Big Data

With the global shortage of resources and energy and the intensification of environmental pollution, the problems of energy consumption and pollution emission in the manufacturing industry have become increasingly prominent. Green development, quality improvement and efficiency increase have gradually become an important development trend of the manufacturing industry. Green manufacturing engineering must be vigorously constructed and developed. Discrete manufacturing is characterized by discontinuous processes. The manufacturing process is accompanied by a large amount of primary energy consumption and environmental emissions. Compared with process manufacturing, discrete manufacturing process control is more complex and changeable, its green development level needs to be improved, and the implementation process is more difficult. As one of the common workshops in discrete manufacturing, NC workshop has problems such as high energy consumption. Based on the above reasons, this paper takes the energy-saving optimization and energy management of discrete manufacturing enterprises as the research goal, and carries out the research on energy-saving optimization and energy management system for NC workshop. It is of great significance to reduce the energy consumption of NC workshop, improve the energy utilization efficiency, solve the pain points of many enterprise information islands and difficult to control energy consumption, and improve the green level of production process in discrete manufacturing enterprises.

A New MINLP Continuous Time Formulation for Scheduling Optimization of Oil Refinery with Unreliable CDUs

Short-term scheduling of oil refinery operations is a complicated optimization problem that requires a high level of detail and needs efficient approaches and software tools. Oil refineries are naturally categorized as continuous process industries due to their production processes, which have essentially different characteristics and constraints from discrete manufacturing processes. It is a significant problem to design effective approaches for oil refinery scheduling optimization. Therefore, this paper aims to provide solutions to the above very challenging problem by proposing a novel methodology. The problem is how to optimize crude oil unloading to charging tanks and charging schedules for different types of crude oils to distillation units (CDUs). Several realistic operational features are considered, such as different arrival times for crude oils, continuous operation of CDUs, no rework of crude oils, and the ability to perform preventative maintenance on CDUs. To solve this problem effectively, a new mixed-integer nonlinear programming (MINLP) model is developed. Industrial case studies are used to demonstrate the effectiveness of the proposed formulation. The computational results show that the case studies are effectively solved with the proposed solution approach.

Multi-objective reconfigurable production line scheduling forsmart home appliances

In a typical discrete manufacturing process, a new type of reconfigurable production line is introduced, which aims to help small- and mid-size enterprises to improve machine utilization and reduce production cost. In order to effectively handle the production scheduling problem for the manufacturing system, an improved multi-objective particle swarm optimization algorithm based on Brownian motion (MOPSO-BM) is proposed. Since the existing MOPSO algorithms are easily stuck in the local optimum, the global search ability of the proposed method is enhanced based on the random motion mechanism of the BM. To further strengthen the global search capacity, a strategy of fitting the inertia weight with the piecewise Gaussian cumulative distribution function (GCDF) is included, which helps to maintain an excellent convergence rate of the algorithm. Based on the commonly used indicators generational distance (GD) and hypervolume (HV), we compare the MOPSO-BM with several other latest algorithms on the benchmark functions, and it shows a better overall performance. Furthermore, for a real reconfigurable production line of smart home appliances, three algorithms, namely non-dominated sorting genetic algorithm-II (NSGA-II), decomposition-based MOPSO (dMOPSO) and MOPSO-BM, are applied to tackle the scheduling problem. It is demonstrated that MOPSO-BM outperforms the others in terms of convergence rate and quality of solutions.

Study on Discrete Manufacturing Quality Control Technology Based on Big Data and Pattern Recognition

Aiming at the quality control problems in the discrete manufacturing process of large and superlarge equipment, which cannot meet the urgent needs of production, a quality control method based on big data and pattern recognition is proposed. A large amount of data is collected through the test equipment developed in the discrete manufacturing process; a database of typical working conditions and an information tracking system relying on the cloud platform were formed. The working conditions were divided by the principal component analysis (PCA) and improved K-means algorithm. The Markov prediction model predicts the working conditions, recognizes the pattern with typical working conditions, regulates the processing parameters, and achieves quality control. Taking the quality control of the hydraulic cylinder manufacturing process above 5 m as an example for experimental verification, the experiments indicated that working conditions can be automatically identified and classified through pattern recognition technology. The process capability index Cpk increased from 0.6 to 1, which proved the effectiveness of quality control and the improvement of processing capabilities.

Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing

The early and robust detection of anomalies occurring in discrete manufacturing processes allows operators to prevent harm, e.g. defects in production machinery or products. While current approaches for data-driven anomaly detection provide good results on the exact processes they were trained on, they often lack the ability to flexibly adapt to changes, e.g. in products. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks. Therefore, this article discusses different continual learning approaches from the group of regularization strategies, which are implemented, evaluated, and compared based on a real industrial metal forming dataset.

Integrated Modelling and Simulation Method for the Project-Type Manufacturing Process: Shipyard Dock Shop Hoisting Process

The dock shop hoisting process is the bottleneck stage of the entire shipbuilding lifecycle, with a crucial influence on a shipyard's competitive goals of cost and delivery time. The complexity and particularity of the hoisting process, which tends to be a project-type manufacturing process, leads to more difficulties in exploiting potentialities for a productivity improvement via computer simulation technology. In order to overcome the deficiency of the previous modelling and simulation methods in terms of the applicability to project-type manufacturing cases, the characteristics of the hoisting process are carefully investigated, followed by a discussion of the specific requirements of constructing a simulation suitable for this kind of process. Then, we apply a task-centric idea to establish an information flow and work flow integrated model (named IWI) that can properly abstract the dynamic behavior of the hoisting process, in which the interrelations between the production elements are quite different from those in the general discrete manufacturing process. According to the mechanism revealed by the IWI model, a simulation algorithm is further designed as the foundation to realize a subsequently developed prototype system. The validity of the proposed modelling and simulation method is tested with a case study on a group of actual dock shop hoisting processes, with multiple indices indicating how well the production system performs output through the simulation experiment.

Quality Prediction of Discrete Manufacturing Process Based on CGAN&Catboost Hybrid Model

Accurate prediction of product quality can be used to control the online production process of workpieces, an appropriate prediction model can effectively improve the yield of industrial production. In order to obtain missing data, the classic method is to regress them one by one. However, the traditional regression prediction method destroys the correlation of the data and cannot use the quality inspection index efficiently. This paper presents an improved hybrid model. The essence lies in a novel easily pluggable CGAN module that refines the previous step of regression data. compared with the regression method, the CGAN module can learn the joint d istribution of data. The experiment of real data result shows that our method has high accuracy compared with the existing ones

A synergistic Mahalanobis–Taguchi system and support vector regression based predictive multivariate manufacturing process quality control approach

The primary objective of this study is to propose and verify a new synergistic prediction-based multivariate process quality control (MPQC) approach for manufacturing processes. The proposed approach considers the influence of covariates (e.g. uncontrollable inputs) and output (or response) uncertainties to predict, monitor, diagnose, and adjust for out-of-control scenarios. The prediction-based real-time synergistic approach integrates off-line and on-line multivariate quality control strategies. In this approach, based on a current state prediction of responses, process control variables are adjusted to prevent any out-of-control or abnormal situations in the process. The unique approach is designed based on a Mahalanobis–Taguchi System (MTS), support vector regression (SVR), bootstrap prediction interval (PI), and derivative-free Nelder-Mead (NM) optimisation strategy. Two real-life case studies demonstrate the suitability of the proposed approach and show improvements in process performance. This easy-to-implement distribution-free predictive quality control approach provides the necessary flexibility to industry practitioners for real-life implementation in discrete or continuous manufacturing processes.

Quality Prediction and Yield Improvement in Process Manufacturing Based on Data Analytics

Quality management is important for maximizing yield in continuous-flow manufacturing. However, it is more difficult to manage quality in continuous-flow manufacturing than in discrete manufacturing because partial defects can significantly affect the quality of an entire lot of final product. In this paper, a comprehensive framework that consists of three steps is proposed to predict defects and improve yield by using semi-supervised learning, time-series analysis, and classification model. In Step 1, semi-supervised learning using both labeled and unlabeled data is applied to generate quality values. In addition, feature values are predicted in time-series analysis in Step 2. Finally, in Step 3, we predict quality values based on the data obtained in Step 1 and Step 2 and calculate yield values with the use of the predicted value. Compared to a conventional production plan, the suggested plan increases yield by up to 8.7%. The production plan proposed in this study is expected to contribute to not only the continuous manufacturing process but the discrete manufacturing process. In addition, it can be used in early diagnosis of equipment failure.